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    <title>ArXiv CS.CV Papers (Image/Video Generation) - April 28, 2025</title>
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        <h1>AIGC Daily Papers</h1>
        <p>Daily papers related to Image/Video/Multimodal Generation from cs.CV</p>
        <p>April 28, 2025</p>
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    <div class="bento-grid" id="paper-grid">
        
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            <h2 class="paper-title">VIST-GPT: Ushering in the Era of Visual Storytelling with LLMs?</h2>
            <p class="paper-summary">Visual storytelling is an interdisciplinary field combining computer vision
and natural language processing to generate cohesive narratives from sequences
of images. This paper presents a novel approach that leverages recent
advancements in multimodal models, specifically adapting transformer-based
architectures and large multimodal models, for the visual storytelling task.
Leveraging the large-scale Visual Storytelling (VIST) dataset, our VIST-GPT
model produces visually grounded, contextually appropriate narratives. We
address the limitations of traditional evaluation metrics, such as BLEU,
METEOR, ROUGE, and CIDEr, which are not suitable for this task. Instead, we
utilize RoViST and GROOVIST, novel reference-free metrics designed to assess
visual storytelling, focusing on visual grounding, coherence, and
non-redundancy. These metrics provide a more nuanced evaluation of narrative
quality, aligning closely with human judgment.</p>
            
            <p class="paper-tldr"><strong>TLDR</strong>: this paper introduces vist-gpt, a transformer-based large multimodal model for visual storytelling using the vist dataset, and proposes rovist and groovist, new reference-free metrics for evaluating visual storytelling quality.</p>
            
            
            <p class="paper-tldr"><strong>TLDR</strong>: 本文介绍了vist-gpt，一个基于transformer的大型多模态模型，用于使用vist数据集进行视觉故事讲述，并提出了rovist和groovist，用于评估视觉故事讲述质量的新型无参考指标。</p>
            

            
            
            <div class="paper-sub-ratings" style="display: flex; flex-wrap: wrap; gap: 10px; margin-bottom: 5px; font-size: 0.8em;">
                
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                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Novelty:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(7/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Clarity:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i>
                    <span class="text-xs text-gray-500 ml-1">(9/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Potential Impact:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
            </div>
            
            

            
            <div class="paper-rating">
                <span class="rating-label" style="color: #000; font-weight: bold;">Overall:</span>
                
                
                    
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                <span class="text-xs text-gray-500 ml-1">(8/10)</span>
            </div>
            

            <a href="http://arxiv.org/abs/2504.19267v1" target="_blank" class="paper-link">
                <i class="fas fa-file-pdf mr-1"></i> Read Paper (PDF)
            </a>
            
            <p class="paper-authors">Authors: Mohamed Gado, Towhid Taliee, Muhammad Memon, Dmitry Ignatov, Radu Timofte</p>
            
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            <h2 class="paper-title">Sketch2Anim: Towards Transferring Sketch Storyboards into 3D Animation</h2>
            <p class="paper-summary">Storyboarding is widely used for creating 3D animations. Animators use the 2D
sketches in storyboards as references to craft the desired 3D animations
through a trial-and-error process. The traditional approach requires
exceptional expertise and is both labor-intensive and time-consuming.
Consequently, there is a high demand for automated methods that can directly
translate 2D storyboard sketches into 3D animations. This task is
under-explored to date and inspired by the significant advancements of motion
diffusion models, we propose to address it from the perspective of conditional
motion synthesis. We thus present Sketch2Anim, composed of two key modules for
sketch constraint understanding and motion generation. Specifically, due to the
large domain gap between the 2D sketch and 3D motion, instead of directly
conditioning on 2D inputs, we design a 3D conditional motion generator that
simultaneously leverages 3D keyposes, joint trajectories, and action words, to
achieve precise and fine-grained motion control. Then, we invent a neural
mapper dedicated to aligning user-provided 2D sketches with their corresponding
3D keyposes and trajectories in a shared embedding space, enabling, for the
first time, direct 2D control of motion generation. Our approach successfully
transfers storyboards into high-quality 3D motions and inherently supports
direct 3D animation editing, thanks to the flexibility of our multi-conditional
motion generator. Comprehensive experiments and evaluations, and a user
perceptual study demonstrate the effectiveness of our approach.</p>
            
            <p class="paper-tldr"><strong>TLDR</strong>: the paper introduces sketch2anim, a novel approach using conditional motion synthesis and a neural mapper to translate 2d storyboard sketches into 3d animations, enabling direct 2d control and 3d editing of motion generation.</p>
            
            
            <p class="paper-tldr"><strong>TLDR</strong>: 本文介绍 sketch2anim，一种新颖的方法，使用条件运动合成和神经映射器将 2d 故事板草图转换为 3d 动画，从而实现对运动生成的直接 2d 控制和 3d 编辑。</p>
            

            
            
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                    <span class="text-xs text-gray-500 ml-1">(7/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Novelty:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Clarity:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i>
                    <span class="text-xs text-gray-500 ml-1">(9/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Potential Impact:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
            </div>
            
            

            
            <div class="paper-rating">
                <span class="rating-label" style="color: #000; font-weight: bold;">Overall:</span>
                
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
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                <span class="text-xs text-gray-500 ml-1">(8/10)</span>
            </div>
            

            <a href="http://arxiv.org/abs/2504.19189v1" target="_blank" class="paper-link">
                <i class="fas fa-file-pdf mr-1"></i> Read Paper (PDF)
            </a>
            
            <p class="paper-authors">Authors: Lei Zhong, Chuan Guo, Yiming Xie, Jiawei Wang, Changjian Li</p>
            
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            <h2 class="paper-title">IM-Portrait: Learning 3D-aware Video Diffusion for PhotorealisticTalking Heads from Monocular Videos</h2>
            <p class="paper-summary">We propose a novel 3D-aware diffusion-based method for generating
photorealistic talking head videos directly from a single identity image and
explicit control signals (e.g., expressions). Our method generates Multiplane
Images (MPIs) that ensure geometric consistency, making them ideal for
immersive viewing experiences like binocular videos for VR headsets. Unlike
existing methods that often require a separate stage or joint optimization to
reconstruct a 3D representation (such as NeRF or 3D Gaussians), our approach
directly generates the final output through a single denoising process,
eliminating the need for post-processing steps to render novel views
efficiently. To effectively learn from monocular videos, we introduce a
training mechanism that reconstructs the output MPI randomly in either the
target or the reference camera space. This approach enables the model to
simultaneously learn sharp image details and underlying 3D information.
Extensive experiments demonstrate the effectiveness of our method, which
achieves competitive avatar quality and novel-view rendering capabilities, even
without explicit 3D reconstruction or high-quality multi-view training data.</p>
            
            <p class="paper-tldr"><strong>TLDR</strong>: the paper introduces a novel 3d-aware diffusion method, im-portrait, for photorealistic talking head video generation from monocular videos using mpis, eliminating the need for explicit 3d reconstruction and post-processing.</p>
            
            
            <p class="paper-tldr"><strong>TLDR</strong>: 该论文提出了一种新的基于扩散的3d感知方法im-portrait，用于从单目视频生成逼真的说话人头部视频，使用mpis，无需显式的3d重建和后处理。</p>
            

            
            
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                    <span class="text-xs text-gray-500 ml-1">(9/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Novelty:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
                
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                    <span class="rating-label">Clarity:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Potential Impact:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(7/10)</span>
                </div>
                
            </div>
            
            

            
            <div class="paper-rating">
                <span class="rating-label" style="color: #000; font-weight: bold;">Overall:</span>
                
                
                    
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                <span class="text-xs text-gray-500 ml-1">(8/10)</span>
            </div>
            

            <a href="http://arxiv.org/abs/2504.19165v1" target="_blank" class="paper-link">
                <i class="fas fa-file-pdf mr-1"></i> Read Paper (PDF)
            </a>
            
            <p class="paper-authors">Authors: Yuan Li, Ziqian Bai, Feitong Tan, Zhaopeng Cui, Sean Fanello, Yinda Zhang</p>
            
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            <h2 class="paper-title">Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions</h2>
            <p class="paper-summary">Generative AI is reshaping art, gaming, and most notably animation. Recent
breakthroughs in foundation and diffusion models have reduced the time and cost
of producing animated content. Characters are central animation components,
involving motion, emotions, gestures, and facial expressions. The pace and
breadth of advances in recent months make it difficult to maintain a coherent
view of the field, motivating the need for an integrative review. Unlike
earlier overviews that treat avatars, gestures, or facial animation in
isolation, this survey offers a single, comprehensive perspective on all the
main generative AI applications for character animation. We begin by examining
the state-of-the-art in facial animation, expression rendering, image
synthesis, avatar creation, gesture modeling, motion synthesis, object
generation, and texture synthesis. We highlight leading research, practical
deployments, commonly used datasets, and emerging trends for each area. To
support newcomers, we also provide a comprehensive background section that
introduces foundational models and evaluation metrics, equipping readers with
the knowledge needed to enter the field. We discuss open challenges and map
future research directions, providing a roadmap to advance AI-driven
character-animation technologies. This survey is intended as a resource for
researchers and developers entering the field of generative AI animation or
adjacent fields. Resources are available at:
https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey.</p>
            
            <p class="paper-tldr"><strong>TLDR</strong>: this paper presents a comprehensive survey of generative ai techniques for character animation, covering various aspects from facial animation to motion synthesis and offering a roadmap for future research.</p>
            
            
            <p class="paper-tldr"><strong>TLDR</strong>: 该论文全面调研了用于角色动画的生成式人工智能技术，涵盖了从面部动画到运动合成的各个方面，并为未来的研究提供了路线图。</p>
            

            
            
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                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
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                <div class="rating-item">
                    <span class="rating-label">Novelty:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(7/10)</span>
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                <div class="rating-item">
                    <span class="rating-label">Clarity:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i>
                    <span class="text-xs text-gray-500 ml-1">(9/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Potential Impact:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
            </div>
            
            

            
            <div class="paper-rating">
                <span class="rating-label" style="color: #000; font-weight: bold;">Overall:</span>
                
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
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                <span class="text-xs text-gray-500 ml-1">(8/10)</span>
            </div>
            

            <a href="http://arxiv.org/abs/2504.19056v1" target="_blank" class="paper-link">
                <i class="fas fa-file-pdf mr-1"></i> Read Paper (PDF)
            </a>
            
            <p class="paper-authors">Authors: Mohammad Mahdi Abootorabi, Omid Ghahroodi, Pardis Sadat Zahraei, Hossein Behzadasl, Alireza Mirrokni, Mobina Salimipanah, Arash Rasouli, Bahar Behzadipour, Sara Azarnoush, Benyamin Maleki, Erfan Sadraiye, Kiarash Kiani Feriz, Mahdi Teymouri Nahad, Ali Moghadasi, Abolfazl Eshagh Abianeh, Nizi Nazar, Hamid R. Rabiee, Mahdieh Soleymani Baghshah, Meisam Ahmadi, Ehsaneddin Asgari</p>
            
        </motion.div>
        
        <motion.div
            initial="{ opacity: 0, y: 50, scale: 0.9 }"
            whileInView="{ opacity: 1, y: 0, scale: 1 }"
            viewport="{ once: true, amount: 0.2 }" /* Trigger when 20% is visible */
            transition="{ duration: 0.5, delay: 0.2, ease: 'easeOut' }"  
            class="bento-item"
            data-motion-element
        >
            <h2 class="paper-title">VI3NR: Variance Informed Initialization for Implicit Neural Representations</h2>
            <p class="paper-summary">Implicit Neural Representations (INRs) are a versatile and powerful tool for
encoding various forms of data, including images, videos, sound, and 3D shapes.
A critical factor in the success of INRs is the initialization of the network,
which can significantly impact the convergence and accuracy of the learned
model. Unfortunately, commonly used neural network initializations are not
widely applicable for many activation functions, especially those used by INRs.
In this paper, we improve upon previous initialization methods by deriving an
initialization that has stable variance across layers, and applies to any
activation function. We show that this generalizes many previous initialization
methods, and has even better stability for well studied activations. We also
show that our initialization leads to improved results with INR activation
functions in multiple signal modalities. Our approach is particularly effective
for Gaussian INRs, where we demonstrate that the theory of our initialization
matches with task performance in multiple experiments, allowing us to achieve
improvements in image, audio, and 3D surface reconstruction.</p>
            
            <p class="paper-tldr"><strong>TLDR</strong>: this paper proposes a novel initialization method for implicit neural representations (inrs) that ensures stable variance across layers and applies to various activation functions, leading to improved performance in image, audio, and 3d surface reconstruction.</p>
            
            
            <p class="paper-tldr"><strong>TLDR</strong>: 本文提出了一种新的隐式神经表示（inr）初始化方法，该方法可确保跨层稳定的方差，并适用于各种激活函数，从而提高了图像、音频和3d表面重建的性能。</p>
            

            
            
            <div class="paper-sub-ratings" style="display: flex; flex-wrap: wrap; gap: 10px; margin-bottom: 5px; font-size: 0.8em;">
                
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                    <span class="text-xs text-gray-500 ml-1">(6/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Novelty:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Clarity:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i>
                    <span class="text-xs text-gray-500 ml-1">(9/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Potential Impact:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(7/10)</span>
                </div>
                
            </div>
            
            

            
            <div class="paper-rating">
                <span class="rating-label" style="color: #000; font-weight: bold;">Overall:</span>
                
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star-half-alt"></i>
                    
                
                    
                        <i class="far fa-star"></i>
                    
                
                <span class="text-xs text-gray-500 ml-1">(7/10)</span>
            </div>
            

            <a href="http://arxiv.org/abs/2504.19270v1" target="_blank" class="paper-link">
                <i class="fas fa-file-pdf mr-1"></i> Read Paper (PDF)
            </a>
            
            <p class="paper-authors">Authors: Chamin Hewa Koneputugodage, Yizhak Ben-Shabat, Sameera Ramasinghe, Stephen Gould</p>
            
        </motion.div>
        
        <motion.div
            initial="{ opacity: 0, y: 50, scale: 0.9 }"
            whileInView="{ opacity: 1, y: 0, scale: 1 }"
            viewport="{ once: true, amount: 0.2 }" /* Trigger when 20% is visible */
            transition="{ duration: 0.5, delay: 0.25, ease: 'easeOut' }"  
            class="bento-item"
            data-motion-element
        >
            <h2 class="paper-title">REED-VAE: RE-Encode Decode Training for Iterative Image Editing with Diffusion Models</h2>
            <p class="paper-summary">While latent diffusion models achieve impressive image editing results, their
application to iterative editing of the same image is severely restricted. When
trying to apply consecutive edit operations using current models, they
accumulate artifacts and noise due to repeated transitions between pixel and
latent spaces. Some methods have attempted to address this limitation by
performing the entire edit chain within the latent space, sacrificing
flexibility by supporting only a limited, predetermined set of diffusion
editing operations. We present a RE-encode decode (REED) training scheme for
variational autoencoders (VAEs), which promotes image quality preservation even
after many iterations. Our work enables multi-method iterative image editing:
users can perform a variety of iterative edit operations, with each operation
building on the output of the previous one using both diffusion-based
operations and conventional editing techniques. We demonstrate the advantage of
REED-VAE across a range of image editing scenarios, including text-based and
mask-based editing frameworks. In addition, we show how REED-VAE enhances the
overall editability of images, increasing the likelihood of successful and
precise edit operations. We hope that this work will serve as a benchmark for
the newly introduced task of multi-method image editing. Our code and models
will be available at https://github.com/galmog/REED-VAE</p>
            
            <p class="paper-tldr"><strong>TLDR</strong>: the paper introduces reed-vae, a training scheme for vaes that improves iterative image editing with diffusion models by preserving image quality and enabling multi-method editing.</p>
            
            
            <p class="paper-tldr"><strong>TLDR</strong>: 该论文介绍了 reed-vae，一种 vae 的训练方案，通过保持图像质量和支持多方法编辑，改进了扩散模型的迭代图像编辑。</p>
            

            
            
            <div class="paper-sub-ratings" style="display: flex; flex-wrap: wrap; gap: 10px; margin-bottom: 5px; font-size: 0.8em;">
                
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                    <span class="rating-label">Relevance:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(7/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Novelty:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Clarity:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i>
                    <span class="text-xs text-gray-500 ml-1">(9/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Potential Impact:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(7/10)</span>
                </div>
                
            </div>
            
            

            
            <div class="paper-rating">
                <span class="rating-label" style="color: #000; font-weight: bold;">Overall:</span>
                
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star-half-alt"></i>
                    
                
                    
                        <i class="far fa-star"></i>
                    
                
                <span class="text-xs text-gray-500 ml-1">(7/10)</span>
            </div>
            

            <a href="http://arxiv.org/abs/2504.18989v1" target="_blank" class="paper-link">
                <i class="fas fa-file-pdf mr-1"></i> Read Paper (PDF)
            </a>
            
            <p class="paper-authors">Authors: Gal Almog, Ariel Shamir, Ohad Fried</p>
            
        </motion.div>
        
        <motion.div
            initial="{ opacity: 0, y: 50, scale: 0.9 }"
            whileInView="{ opacity: 1, y: 0, scale: 1 }"
            viewport="{ once: true, amount: 0.2 }" /* Trigger when 20% is visible */
            transition="{ duration: 0.5, delay: 0.30000000000000004, ease: 'easeOut' }"  
            class="bento-item"
            data-motion-element
        >
            <h2 class="paper-title">FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement</h2>
            <p class="paper-summary">The advent of Deep Neural Networks (DNNs) has driven remarkable progress in
low-light image enhancement (LLIE), with diverse architectures (e.g., CNNs and
Transformers) and color spaces (e.g., sRGB, HSV, HVI) yielding impressive
results. Recent efforts have sought to leverage the complementary strengths of
these paradigms, offering promising solutions to enhance performance across
varying degradation scenarios. However, existing fusion strategies are hindered
by challenges such as parameter explosion, optimization instability, and
feature misalignment, limiting further improvements. To overcome these issues,
we introduce FusionNet, a novel multi-model linear fusion framework that
operates in parallel to effectively capture global and local features across
diverse color spaces. By incorporating a linear fusion strategy underpinned by
Hilbert space theoretical guarantees, FusionNet mitigates network collapse and
reduces excessive training costs. Our method achieved 1st place in the CVPR2025
NTIRE Low Light Enhancement Challenge. Extensive experiments conducted on
synthetic and real-world benchmark datasets demonstrate that the proposed
method significantly outperforms state-of-the-art methods in terms of both
quantitative and qualitative results, delivering robust enhancement under
diverse low-light conditions.</p>
            
            <p class="paper-tldr"><strong>TLDR</strong>: fusionnet, a novel multi-model linear fusion framework, enhances low-light images by capturing global and local features across diverse color spaces, achieving state-of-the-art results and winning the cvpr2025 ntire challenge.</p>
            
            
            <p class="paper-tldr"><strong>TLDR</strong>: fusionnet 是一种新颖的多模型线性融合框架，通过捕获不同色彩空间中的全局和局部特征来增强弱光图像，实现了最先进的结果，并赢得了 cvpr2025 ntire 挑战赛。</p>
            

            
            
            <div class="paper-sub-ratings" style="display: flex; flex-wrap: wrap; gap: 10px; margin-bottom: 5px; font-size: 0.8em;">
                
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                    <i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i><i class="far fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(3/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Novelty:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(7/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Clarity:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i>
                    <span class="text-xs text-gray-500 ml-1">(9/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Potential Impact:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(6/10)</span>
                </div>
                
            </div>
            
            

            
            <div class="paper-rating">
                <span class="rating-label" style="color: #000; font-weight: bold;">Overall:</span>
                
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star-half-alt"></i>
                    
                
                    
                        <i class="far fa-star"></i>
                    
                
                    
                        <i class="far fa-star"></i>
                    
                
                <span class="text-xs text-gray-500 ml-1">(5/10)</span>
            </div>
            

            <a href="http://arxiv.org/abs/2504.19295v1" target="_blank" class="paper-link">
                <i class="fas fa-file-pdf mr-1"></i> Read Paper (PDF)
            </a>
            
            <p class="paper-authors">Authors: Kangbiao Shi, Yixu Feng, Tao Hu, Yu Cao, Peng Wu, Yijin Liang, Yanning Zhang, Qingsen Yan</p>
            
        </motion.div>
        
        <motion.div
            initial="{ opacity: 0, y: 50, scale: 0.9 }"
            whileInView="{ opacity: 1, y: 0, scale: 1 }"
            viewport="{ once: true, amount: 0.2 }" /* Trigger when 20% is visible */
            transition="{ duration: 0.5, delay: 0.35000000000000003, ease: 'easeOut' }"  
            class="bento-item"
            data-motion-element
        >
            <h2 class="paper-title">Improving Generalization in MRI-Based Deep Learning Models for Total Knee Replacement Prediction</h2>
            <p class="paper-summary">Knee osteoarthritis (KOA) is a common joint disease that causes pain and
mobility issues. While MRI-based deep learning models have demonstrated
superior performance in predicting total knee replacement (TKR) and disease
progression, their generalizability remains challenging, particularly when
applied to imaging data from different sources. In this study, we have shown
that replacing batch normalization with instance normalization, using data
augmentation, and applying contrastive loss improves model generalization in a
baseline deep learning model for knee osteoarthritis (KOA) prediction. We
trained and evaluated our model using MRI data from the Osteoarthritis
Initiative (OAI) database, considering sagittal fat-suppressed
intermediate-weighted turbo spin-echo (FS-IW-TSE) images as the source domain
and sagittal fat-suppressed three-dimensional (3D) dual-echo in steady state
(DESS) images as the target domain. The results demonstrate a statistically
significant improvement in classification accuracy across both domains, with
our approach outperforming the baseline model.</p>
            
            <p class="paper-tldr"><strong>TLDR</strong>: this paper improves the generalization of deep learning models for total knee replacement prediction using mri data by implementing instance normalization, data augmentation, and contrastive loss, demonstrating statistically significant accuracy improvements across different mri domains.</p>
            
            
            <p class="paper-tldr"><strong>TLDR</strong>: 本文通过使用实例归一化、数据增强和对比损失，提高了基于mri的深度学习模型在全膝关节置换预测中的泛化能力，并在不同mri领域中展示了统计学上显著的准确性提升。</p>
            

            
            
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                    <i class="fas fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(2/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Novelty:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star-half-alt"></i><i class="far fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(5/10)</span>
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                <div class="rating-item">
                    <span class="rating-label">Clarity:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Potential Impact:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(6/10)</span>
                </div>
                
            </div>
            
            

            
            <div class="paper-rating">
                <span class="rating-label" style="color: #000; font-weight: bold;">Overall:</span>
                
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="far fa-star"></i>
                    
                
                    
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                        <i class="far fa-star"></i>
                    
                
                <span class="text-xs text-gray-500 ml-1">(4/10)</span>
            </div>
            

            <a href="http://arxiv.org/abs/2504.19203v1" target="_blank" class="paper-link">
                <i class="fas fa-file-pdf mr-1"></i> Read Paper (PDF)
            </a>
            
            <p class="paper-authors">Authors: Ehsan Karami, Hamid Soltanian-Zadeh</p>
            
        </motion.div>
        
        <motion.div
            initial="{ opacity: 0, y: 50, scale: 0.9 }"
            whileInView="{ opacity: 1, y: 0, scale: 1 }"
            viewport="{ once: true, amount: 0.2 }" /* Trigger when 20% is visible */
            transition="{ duration: 0.5, delay: 0.4, ease: 'easeOut' }"  
            class="bento-item"
            data-motion-element
        >
            <h2 class="paper-title">Blind Source Separation Based on Sparsity</h2>
            <p class="paper-summary">Blind source separation (BSS) is a key technique in array processing and data
analysis, aiming to recover unknown sources from observed mixtures without
knowledge of the mixing matrix. Classical independent component analysis (ICA)
methods rely on the assumption that sources are mutually independent. To
address limitations of ICA, sparsity-based methods have been introduced, which
decompose source signals sparsely in a predefined dictionary. Morphological
Component Analysis (MCA), based on sparse representation theory, assumes that a
signal is a linear combination of components with distinct geometries, each
sparsely representable in one dictionary and not in others. This approach has
recently been applied to BSS with promising results.
  This report reviews key approaches derived from classical ICA and explores
sparsity-based methods for BSS. It introduces the theory of sparse
representation and decomposition, followed by a block coordinate relaxation MCA
algorithm, whose variants are used in Multichannel MCA (MMCA) and Generalized
MCA (GMCA). A local dictionary learning method using K-SVD is then presented.
Finally, we propose an improved algorithm, SAC+BK-SVD, which enhances K-SVD by
learning a block-sparsifying dictionary that clusters and updates similar atoms
in blocks.
  The implementation includes experiments on image segmentation and blind image
source separation using the discussed techniques. We also compare the proposed
block-sparse dictionary learning algorithm with K-SVD. Simulation results
demonstrate that our method yields improved blind image separation quality.</p>
            
            <p class="paper-tldr"><strong>TLDR</strong>: this paper reviews sparsity-based blind source separation (bss) methods, proposes an improved dictionary learning algorithm (sac+bk-svd), and demonstrates its effectiveness in image segmentation and blind image separation.</p>
            
            
            <p class="paper-tldr"><strong>TLDR</strong>: 本文回顾了基于稀疏性的盲源分离(bss)方法，提出了一种改进的字典学习算法(sac+bk-svd)，并证明了其在图像分割和盲图像分离中的有效性。</p>
            

            
            
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                    <i class="fas fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(2/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Novelty:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(6/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Clarity:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(8/10)</span>
                </div>
                
                
                <div class="rating-item">
                    <span class="rating-label">Potential Impact:</span>
                    
                    <i class="fas fa-star"></i><i class="fas fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i><i class="far fa-star"></i>
                    <span class="text-xs text-gray-500 ml-1">(4/10)</span>
                </div>
                
            </div>
            
            

            
            <div class="paper-rating">
                <span class="rating-label" style="color: #000; font-weight: bold;">Overall:</span>
                
                
                    
                        <i class="fas fa-star"></i>
                    
                
                    
                        <i class="fas fa-star-half-alt"></i>
                    
                
                    
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                        <i class="far fa-star"></i>
                    
                
                <span class="text-xs text-gray-500 ml-1">(3/10)</span>
            </div>
            

            <a href="http://arxiv.org/abs/2504.19124v1" target="_blank" class="paper-link">
                <i class="fas fa-file-pdf mr-1"></i> Read Paper (PDF)
            </a>
            
            <p class="paper-authors">Authors: Zhongxuan Li</p>
            
        </motion.div>
        
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